I am analyzing an experiment where I have samples from multiple regions of the brain, in a number of different conditions. We are only interested in differences within brain regions, not between them.
Is it recommended to combine the tissues into a single model, or should they be analyzed separately?
My initial thought was that increasing the number of samples should provide a better estimate of the mean-variance relationship, and increase the power to detect differences. When comparing the two approaches some regions seem to gain power to detect differences, while other lose power when combined into a single model.
Thank you, I'm guessing I could plot the average logCPMs of the tissues against each other to determine if the expression differs, is it possible to use e.g.
plotBCV
orplotQLDisp
to determine if the tissues differ in variability?Just plot the samples with plotMDS().
Alternatively you would use
voomLmFit()
withsample.weights=TRUE
orvar.group=tissue
. In principle, you could even usevoomByGroup()
withgroup=tissue
(https://github.com/YOU-k/voomByGroup). Either approach would equalize any systematic differencees in variability between tissue. I'm not necessarily recommending these approaches, just listing them as possiblities.